Object Tracking with Optical Flow

In this lesson we'll learn:

1. How to use Optical Flow in OpenCV
2. Then use Dense Optical Flow

# Our Setup, Import Libaries, Create our Imshow Function and Download our Images
import cv2
import numpy as np
from matplotlib import pyplot as plt

# Define our imshow function 
def imshow(title = "Image", image = None, size = 10):
    w, h = image.shape[0], image.shape[1]
    aspect_ratio = w/h
    plt.figure(figsize=(size * aspect_ratio,size))
    plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
    plt.title(title)
    plt.show()

The Lucas-Kanade Optical Flow Algorithm

Optical flow is the pattern of apparent motion of image objects between two consecutive frames caused by the movemement of object or camera. It is 2D vector field where each vector is a displacement vector showing the movement of points from first frame to second. Consider the image below (Image Courtesy: Wikipedia article on Optical Flow).

Uzbek

Optik oqim - bu ob'ekt yoki kameraning harakati natijasida hosil bo'lgan ikkita ketma-ket kadrlar orasidagi tasvir ob'ektlarining ko'rinadigan harakati. Bu 2D vektor maydoni bo'lib, har bir vektor nuqtalarning birinchi kadrdan ikkinchisiga harakatini ko'rsatadigan siljish vektoridir. Ya'ni kadrdan kadrga o'tganda o'sha pixel boshqa joyga siljiydi. Yani siljish vektorlariga qarab harakatlanish

It shows a ball moving in 5 consecutive frames. The arrow shows its displacement vector. Optical flow has many applications in areas like :

• Structure from Motion
• Video Compression
• Video Stabilization

Optical flow works on several assumptions:

• The pixel intensities of an object do not change between consecutive frames.
• Neighbouring pixels have similar motion.

Learn more - https://learnopencv.com/optical-flow-in-opencv/

# Load video stream, short clip
#cap = cv2.VideoCapture('walking_short_clip.mp4')


cap = cv2.VideoCapture(0)

# Get the height and width of the frame (required to be an interger)
width = int(cap.get(3)) 
height = int(cap.get(4))


# Set parameters for ShiTomasi corner detection
feature_params = dict( maxCorners = 100,
                      qualityLevel = 0.3,
                      minDistance = 7,
                      blockSize = 7 )

# Set parameters for lucas kanade optical flow
lucas_kanade_params = dict( winSize  = (15,15),
                           maxLevel = 2,
                           criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))

# Create some random colors for drawing purposes
color = np.random.randint(0,255,(100,3))

# Take first frame and find corners in it
ret, prev_frame = cap.read()
prev_gray = cv2.cvtColor(prev_frame, cv2.COLOR_BGR2GRAY)

# Birinchi framedagi cornerlarni topib olamiz. 
prev_corners = cv2.goodFeaturesToTrack(prev_gray, mask = None, **feature_params)

# Create a mask image for drawing purposes
mask = np.zeros_like(prev_frame)

while True:
    ret, frame = cap.read()

    if ret == True:
        frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

        # calculate optical flow
        new_corners, status, errors = cv2.calcOpticalFlowPyrLK(prev_gray, 
                                                               frame_gray, 
                                                               prev_corners, 
                                                               None, 
                                                               **lucas_kanade_params)

        # Select and store good points
        good_new = new_corners[status==1]
        good_old = prev_corners[status==1]

        # Draw the tracks
        for i,(new,old) in enumerate(zip(good_new, good_old)):
            a, b = new.ravel()
            c, d = old.ravel()
            mask = cv2.line(mask, (int(a),int(b)),(int(c),int(d)), color[i].tolist(), 2)
            frame = cv2.circle(frame, (int(a),int(b)), 5, color[i].tolist(),-1)

            img = cv2.add(frame,mask)

            # Show Optical Flow
            imshow('Optical Flow - Lucas-Kanade',img)
            cv2.imshow('Optical Flow - Lucas-Kanade',img)
            # Now update the previous frame and previous points
            prev_gray = frame_gray.copy()
            prev_corners = good_new.reshape(-1,1,2)
        
            if cv2.waitKey(1) & 0xFF == ord('q'):
                break
    else:
        break
        
        

cap.release()
cv2.destroyAllWindows()

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
Input In [3], in <cell line: 36>()
     60 img = cv2.add(frame,mask)
     62 # Show Optical Flow
---> 63 imshow('Optical Flow - Lucas-Kanade',img)
     64 cv2.imshow('Optical Flow - Lucas-Kanade',img)
     65 # Now update the previous frame and previous points

Input In [1], in imshow(title, image, size)
     11 plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
     12 plt.title(title)
---> 13 plt.show()

File ~\anaconda3\envs\data_science\lib\site-packages\matplotlib\pyplot.py:368, in show(*args, **kwargs)
    324 """
    325 Display all open figures.
    326 
   (...)
    365 explicitly there.
    366 """
    367 _warn_if_gui_out_of_main_thread()
--> 368 return _backend_mod.show(*args, **kwargs)

File ~\anaconda3\envs\data_science\lib\site-packages\matplotlib_inline\backend_inline.py:41, in show(close, block)
     39 try:
     40     for figure_manager in Gcf.get_all_fig_managers():
---> 41         display(
     42             figure_manager.canvas.figure,
     43             metadata=_fetch_figure_metadata(figure_manager.canvas.figure)
     44         )
     45 finally:
     46     show._to_draw = []

File ~\anaconda3\envs\data_science\lib\site-packages\IPython\core\display_functions.py:298, in display(include, exclude, metadata, transient, display_id, raw, clear, *objs, **kwargs)
    296     publish_display_data(data=obj, metadata=metadata, **kwargs)
    297 else:
--> 298     format_dict, md_dict = format(obj, include=include, exclude=exclude)
    299     if not format_dict:
    300         # nothing to display (e.g. _ipython_display_ took over)
    301         continue

File ~\anaconda3\envs\data_science\lib\site-packages\IPython\core\formatters.py:178, in DisplayFormatter.format(self, obj, include, exclude)
    176 md = None
    177 try:
--> 178     data = formatter(obj)
    179 except:
    180     # FIXME: log the exception
    181     raise

File ~\anaconda3\envs\data_science\lib\site-packages\decorator.py:232, in decorate.<locals>.fun(*args, **kw)
    230 if not kwsyntax:
    231     args, kw = fix(args, kw, sig)
--> 232 return caller(func, *(extras + args), **kw)

File ~\anaconda3\envs\data_science\lib\site-packages\IPython\core\formatters.py:222, in catch_format_error(method, self, *args, **kwargs)
    220 """show traceback on failed format call"""
    221 try:
--> 222     r = method(self, *args, **kwargs)
    223 except NotImplementedError:
    224     # don't warn on NotImplementedErrors
    225     return self._check_return(None, args[0])

File ~\anaconda3\envs\data_science\lib\site-packages\IPython\core\formatters.py:339, in BaseFormatter.__call__(self, obj)
    337     pass
    338 else:
--> 339     return printer(obj)
    340 # Finally look for special method names
    341 method = get_real_method(obj, self.print_method)

File ~\anaconda3\envs\data_science\lib\site-packages\IPython\core\pylabtools.py:151, in print_figure(fig, fmt, bbox_inches, base64, **kwargs)
    148     from matplotlib.backend_bases import FigureCanvasBase
    149     FigureCanvasBase(fig)
--> 151 fig.canvas.print_figure(bytes_io, **kw)
    152 data = bytes_io.getvalue()
    153 if fmt == 'svg':

File ~\anaconda3\envs\data_science\lib\site-packages\matplotlib\backend_bases.py:2319, in FigureCanvasBase.print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)
   2315 try:
   2316     # _get_renderer may change the figure dpi (as vector formats
   2317     # force the figure dpi to 72), so we need to set it again here.
   2318     with cbook._setattr_cm(self.figure, dpi=dpi):
-> 2319         result = print_method(
   2320             filename,
   2321             facecolor=facecolor,
   2322             edgecolor=edgecolor,
   2323             orientation=orientation,
   2324             bbox_inches_restore=_bbox_inches_restore,
   2325             **kwargs)
   2326 finally:
   2327     if bbox_inches and restore_bbox:

File ~\anaconda3\envs\data_science\lib\site-packages\matplotlib\backend_bases.py:1648, in _check_savefig_extra_args.<locals>.wrapper(*args, **kwargs)
   1640     _api.warn_deprecated(
   1641         '3.3', name=name, removal='3.6',
   1642         message='%(name)s() got unexpected keyword argument "'
   1643                 + arg + '" which is no longer supported as of '
   1644                 '%(since)s and will become an error '
   1645                 '%(removal)s')
   1646     kwargs.pop(arg)
-> 1648 return func(*args, **kwargs)

File ~\anaconda3\envs\data_science\lib\site-packages\matplotlib\_api\deprecation.py:412, in delete_parameter.<locals>.wrapper(*inner_args, **inner_kwargs)
    402     deprecation_addendum = (
    403         f"If any parameter follows {name!r}, they should be passed as "
    404         f"keyword, not positionally.")
    405     warn_deprecated(
    406         since,
    407         name=repr(name),
   (...)
    410                  else deprecation_addendum,
    411         **kwargs)
--> 412 return func(*inner_args, **inner_kwargs)

File ~\anaconda3\envs\data_science\lib\site-packages\matplotlib\backends\backend_agg.py:540, in FigureCanvasAgg.print_png(self, filename_or_obj, metadata, pil_kwargs, *args)
    490 @_check_savefig_extra_args
    491 @_api.delete_parameter("3.5", "args")
    492 def print_png(self, filename_or_obj, *args,
    493               metadata=None, pil_kwargs=None):
    494     """
    495     Write the figure to a PNG file.
    496 
   (...)
    538         *metadata*, including the default 'Software' key.
    539     """
--> 540     FigureCanvasAgg.draw(self)
    541     mpl.image.imsave(
    542         filename_or_obj, self.buffer_rgba(), format="png", origin="upper",
    543         dpi=self.figure.dpi, metadata=metadata, pil_kwargs=pil_kwargs)

File ~\anaconda3\envs\data_science\lib\site-packages\matplotlib\backends\backend_agg.py:436, in FigureCanvasAgg.draw(self)
    432 # Acquire a lock on the shared font cache.
    433 with RendererAgg.lock, \
    434      (self.toolbar._wait_cursor_for_draw_cm() if self.toolbar
    435       else nullcontext()):
--> 436     self.figure.draw(self.renderer)
    437     # A GUI class may be need to update a window using this draw, so
    438     # don't forget to call the superclass.
    439     super().draw()

File ~\anaconda3\envs\data_science\lib\site-packages\matplotlib\artist.py:73, in _finalize_rasterization.<locals>.draw_wrapper(artist, renderer, *args, **kwargs)
     71 @wraps(draw)
     72 def draw_wrapper(artist, renderer, *args, **kwargs):
---> 73     result = draw(artist, renderer, *args, **kwargs)
     74     if renderer._rasterizing:
     75         renderer.stop_rasterizing()

File ~\anaconda3\envs\data_science\lib\site-packages\matplotlib\artist.py:50, in allow_rasterization.<locals>.draw_wrapper(artist, renderer)
     47     if artist.get_agg_filter() is not None:
     48         renderer.start_filter()
---> 50     return draw(artist, renderer)
     51 finally:
     52     if artist.get_agg_filter() is not None:

File ~\anaconda3\envs\data_science\lib\site-packages\matplotlib\figure.py:2810, in Figure.draw(self, renderer)
   2807         # ValueError can occur when resizing a window.
   2809 self.patch.draw(renderer)
-> 2810 mimage._draw_list_compositing_images(
   2811     renderer, self, artists, self.suppressComposite)
   2813 for sfig in self.subfigs:
   2814     sfig.draw(renderer)

File ~\anaconda3\envs\data_science\lib\site-packages\matplotlib\image.py:132, in _draw_list_compositing_images(renderer, parent, artists, suppress_composite)
    130 if not_composite or not has_images:
    131     for a in artists:
--> 132         a.draw(renderer)
    133 else:
    134     # Composite any adjacent images together
    135     image_group = []

File ~\anaconda3\envs\data_science\lib\site-packages\matplotlib\artist.py:50, in allow_rasterization.<locals>.draw_wrapper(artist, renderer)
     47     if artist.get_agg_filter() is not None:
     48         renderer.start_filter()
---> 50     return draw(artist, renderer)
     51 finally:
     52     if artist.get_agg_filter() is not None:

File ~\anaconda3\envs\data_science\lib\site-packages\matplotlib\axes\_base.py:3082, in _AxesBase.draw(self, renderer)
   3079         a.draw(renderer)
   3080     renderer.stop_rasterizing()
-> 3082 mimage._draw_list_compositing_images(
   3083     renderer, self, artists, self.figure.suppressComposite)
   3085 renderer.close_group('axes')
   3086 self.stale = False

File ~\anaconda3\envs\data_science\lib\site-packages\matplotlib\image.py:132, in _draw_list_compositing_images(renderer, parent, artists, suppress_composite)
    130 if not_composite or not has_images:
    131     for a in artists:
--> 132         a.draw(renderer)
    133 else:
    134     # Composite any adjacent images together
    135     image_group = []

File ~\anaconda3\envs\data_science\lib\site-packages\matplotlib\artist.py:50, in allow_rasterization.<locals>.draw_wrapper(artist, renderer)
     47     if artist.get_agg_filter() is not None:
     48         renderer.start_filter()
---> 50     return draw(artist, renderer)
     51 finally:
     52     if artist.get_agg_filter() is not None:

File ~\anaconda3\envs\data_science\lib\site-packages\matplotlib\image.py:646, in _ImageBase.draw(self, renderer, *args, **kwargs)
    644         renderer.draw_image(gc, l, b, im, trans)
    645 else:
--> 646     im, l, b, trans = self.make_image(
    647         renderer, renderer.get_image_magnification())
    648     if im is not None:
    649         renderer.draw_image(gc, l, b, im)

File ~\anaconda3\envs\data_science\lib\site-packages\matplotlib\image.py:956, in AxesImage.make_image(self, renderer, magnification, unsampled)
    953 transformed_bbox = TransformedBbox(bbox, trans)
    954 clip = ((self.get_clip_box() or self.axes.bbox) if self.get_clip_on()
    955         else self.figure.bbox)
--> 956 return self._make_image(self._A, bbox, transformed_bbox, clip,
    957                         magnification, unsampled=unsampled)

File ~\anaconda3\envs\data_science\lib\site-packages\matplotlib\image.py:557, in _ImageBase._make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification, unsampled, round_to_pixel_border)
    554     alpha = self._get_scalar_alpha()
    555     output_alpha = _resample(  # resample alpha channel
    556         self, A[..., 3], out_shape, t, alpha=alpha)
--> 557     output = _resample(  # resample rgb channels
    558         self, _rgb_to_rgba(A[..., :3]), out_shape, t, alpha=alpha)
    559     output[..., 3] = output_alpha  # recombine rgb and alpha
    561 # at this point output is either a 2D array of normed data
    562 # (of int or float)
    563 # or an RGBA array of re-sampled input

File ~\anaconda3\envs\data_science\lib\site-packages\matplotlib\image.py:193, in _resample(image_obj, data, out_shape, transform, resample, alpha)
    191 if resample is None:
    192     resample = image_obj.get_resample()
--> 193 _image.resample(data, out, transform,
    194                 _interpd_[interpolation],
    195                 resample,
    196                 alpha,
    197                 image_obj.get_filternorm(),
    198                 image_obj.get_filterrad())
    199 return out

KeyboardInterrupt: 

2. Dense Optical Flow

Lucas-Kanade method computes optical flow for a sparse feature set (in our example, corners detected using Shi-Tomasi algorithm). OpenCV provides another algorithm to find the dense optical flow. It computes the optical flow for all the points in the frame. It is based on Gunner Farneback’s algorithm which is explained in “Two-Frame Motion Estimation Based on Polynomial Expansion” by Gunner Farneback in 2003.

Below sample shows how to find the dense optical flow using above algorithm. We get a 2-channel array with optical flow vectors, (u,v). We find their magnitude and direction. We color code the result for better visualization.

• Direction corresponds to Hue value of the image.
• Magnitude corresponds to Value plane. See the code below:

# Load video stream, long clip
cap = cv2.VideoCapture(0)

# Get the height and width of the frame (required to be an interger)
width = int(cap.get(3)) 
height = int(cap.get(4))

# Get first frame
ret, first_frame = cap.read()
previous_gray = cv2.cvtColor(first_frame, cv2.COLOR_BGR2GRAY)
hsv = np.zeros_like(first_frame)
hsv[...,1] = 255 #1 indexdagi col barchasi oq ranga tenglanyapti. 

while True:
    
    # Read of video file
    ret, frame2 = cap.read()

    if ret == True:
        next_ = cv2.cvtColor(frame2,cv2.COLOR_BGR2GRAY)

        # Computes the dense optical flow using the Gunnar Farneback’s algorithm
        flow = cv2.calcOpticalFlowFarneback(previous_gray, next_, 
                                            None, 0.5, 3, 15, 3, 5, 1.2, 0)

        # use flow to calculate the magnitude (speed) and angle of motion
        # use these values to calculate the color to reflect speed and angle
        magnitude, angle = cv2.cartToPolar(flow[...,0], flow[...,1])
        hsv[...,0] = angle * (180 / (np.pi/2))
        hsv[...,2] = cv2.normalize(magnitude, None, 0, 255, cv2.NORM_MINMAX)
        final = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)

        # Show our demo of Dense Optical Flow
        imshow('Dense Optical Flow', final)
        cv2.imshow('Dense Optical Flow', final)

        # Store current image as previous image
        previous_gray = next_
        
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    else:
          break
    
cap.release()
cv2.destroyAllWindows()

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
Input In [2], in <cell line: 14>()
     31 final = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
     33 # Show our demo of Dense Optical Flow
---> 34 imshow('Dense Optical Flow', final)
     35 cv2.imshow('Dense Optical Flow', final)
     37 # Store current image as previous image

Input In [1], in imshow(title, image, size)
     11 plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
     12 plt.title(title)
---> 13 plt.show()

File ~\anaconda3\envs\data_science\lib\site-packages\matplotlib\pyplot.py:368, in show(*args, **kwargs)
    324 """
    325 Display all open figures.
    326 
   (...)
    365 explicitly there.
    366 """
    367 _warn_if_gui_out_of_main_thread()
--> 368 return _backend_mod.show(*args, **kwargs)

File ~\anaconda3\envs\data_science\lib\site-packages\matplotlib_inline\backend_inline.py:41, in show(close, block)
     39 try:
     40     for figure_manager in Gcf.get_all_fig_managers():
---> 41         display(
     42             figure_manager.canvas.figure,
     43             metadata=_fetch_figure_metadata(figure_manager.canvas.figure)
     44         )
     45 finally:
     46     show._to_draw = []

File ~\anaconda3\envs\data_science\lib\site-packages\IPython\core\display_functions.py:298, in display(include, exclude, metadata, transient, display_id, raw, clear, *objs, **kwargs)
    296     publish_display_data(data=obj, metadata=metadata, **kwargs)
    297 else:
--> 298     format_dict, md_dict = format(obj, include=include, exclude=exclude)
    299     if not format_dict:
    300         # nothing to display (e.g. _ipython_display_ took over)
    301         continue

File ~\anaconda3\envs\data_science\lib\site-packages\IPython\core\formatters.py:178, in DisplayFormatter.format(self, obj, include, exclude)
    176 md = None
    177 try:
--> 178     data = formatter(obj)
    179 except:
    180     # FIXME: log the exception
    181     raise

File ~\anaconda3\envs\data_science\lib\site-packages\decorator.py:232, in decorate.<locals>.fun(*args, **kw)
    230 if not kwsyntax:
    231     args, kw = fix(args, kw, sig)
--> 232 return caller(func, *(extras + args), **kw)

File ~\anaconda3\envs\data_science\lib\site-packages\IPython\core\formatters.py:222, in catch_format_error(method, self, *args, **kwargs)
    220 """show traceback on failed format call"""
    221 try:
--> 222     r = method(self, *args, **kwargs)
    223 except NotImplementedError:
    224     # don't warn on NotImplementedErrors
    225     return self._check_return(None, args[0])

File ~\anaconda3\envs\data_science\lib\site-packages\IPython\core\formatters.py:339, in BaseFormatter.__call__(self, obj)
    337     pass
    338 else:
--> 339     return printer(obj)
    340 # Finally look for special method names
    341 method = get_real_method(obj, self.print_method)

File ~\anaconda3\envs\data_science\lib\site-packages\IPython\core\pylabtools.py:151, in print_figure(fig, fmt, bbox_inches, base64, **kwargs)
    148     from matplotlib.backend_bases import FigureCanvasBase
    149     FigureCanvasBase(fig)
--> 151 fig.canvas.print_figure(bytes_io, **kw)
    152 data = bytes_io.getvalue()
    153 if fmt == 'svg':

File ~\anaconda3\envs\data_science\lib\site-packages\matplotlib\backend_bases.py:2319, in FigureCanvasBase.print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)
   2315 try:
   2316     # _get_renderer may change the figure dpi (as vector formats
   2317     # force the figure dpi to 72), so we need to set it again here.
   2318     with cbook._setattr_cm(self.figure, dpi=dpi):
-> 2319         result = print_method(
   2320             filename,
   2321             facecolor=facecolor,
   2322             edgecolor=edgecolor,
   2323             orientation=orientation,
   2324             bbox_inches_restore=_bbox_inches_restore,
   2325             **kwargs)
   2326 finally:
   2327     if bbox_inches and restore_bbox:

File ~\anaconda3\envs\data_science\lib\site-packages\matplotlib\backend_bases.py:1648, in _check_savefig_extra_args.<locals>.wrapper(*args, **kwargs)
   1640     _api.warn_deprecated(
   1641         '3.3', name=name, removal='3.6',
   1642         message='%(name)s() got unexpected keyword argument "'
   1643                 + arg + '" which is no longer supported as of '
   1644                 '%(since)s and will become an error '
   1645                 '%(removal)s')
   1646     kwargs.pop(arg)
-> 1648 return func(*args, **kwargs)

File ~\anaconda3\envs\data_science\lib\site-packages\matplotlib\_api\deprecation.py:412, in delete_parameter.<locals>.wrapper(*inner_args, **inner_kwargs)
    402     deprecation_addendum = (
    403         f"If any parameter follows {name!r}, they should be passed as "
    404         f"keyword, not positionally.")
    405     warn_deprecated(
    406         since,
    407         name=repr(name),
   (...)
    410                  else deprecation_addendum,
    411         **kwargs)
--> 412 return func(*inner_args, **inner_kwargs)

File ~\anaconda3\envs\data_science\lib\site-packages\matplotlib\backends\backend_agg.py:541, in FigureCanvasAgg.print_png(self, filename_or_obj, metadata, pil_kwargs, *args)
    494 """
    495 Write the figure to a PNG file.
    496 
   (...)
    538     *metadata*, including the default 'Software' key.
    539 """
    540 FigureCanvasAgg.draw(self)
--> 541 mpl.image.imsave(
    542     filename_or_obj, self.buffer_rgba(), format="png", origin="upper",
    543     dpi=self.figure.dpi, metadata=metadata, pil_kwargs=pil_kwargs)

File ~\anaconda3\envs\data_science\lib\site-packages\matplotlib\image.py:1675, in imsave(fname, arr, vmin, vmax, cmap, format, origin, dpi, metadata, pil_kwargs)
   1673 pil_kwargs.setdefault("format", format)
   1674 pil_kwargs.setdefault("dpi", (dpi, dpi))
-> 1675 image.save(fname, **pil_kwargs)

File ~\anaconda3\envs\data_science\lib\site-packages\PIL\Image.py:2300, in Image.save(self, fp, format, **params)
   2297         fp = builtins.open(filename, "w+b")
   2299 try:
-> 2300     save_handler(self, fp, filename)
   2301 except Exception:
   2302     if open_fp:

File ~\anaconda3\envs\data_science\lib\site-packages\PIL\PngImagePlugin.py:1374, in _save(im, fp, filename, chunk, save_all)
   1372     _write_multiple_frames(im, fp, chunk, rawmode)
   1373 else:
-> 1374     ImageFile._save(im, _idat(fp, chunk), [("zip", (0, 0) + im.size, 0, rawmode)])
   1376 if info:
   1377     for info_chunk in info.chunks:

File ~\anaconda3\envs\data_science\lib\site-packages\PIL\ImageFile.py:518, in _save(im, fp, tile, bufsize)
    515 if exc:
    516     # compress to Python file-compatible object
    517     while True:
--> 518         l, s, d = encoder.encode(bufsize)
    519         fp.write(d)
    520         if s:

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